Recommended Data Quality Books

December 5, 2010 by · Comments Off on Recommended Data Quality Books
Filed under: Recommendations 

I am putting together a list of book recommendations for the data quality practitioner, and have added a page link on the web site’s Page Link bar. Click here for a direct link.

Value-Driven Data Quality Projects

November 17, 2010 by · Leave a Comment
Filed under: Business Impacts, Data Governance, Data Quality 

There are roughly two types of justifications for data quality programs. The first leverages a business case or return on investment analysis to identify wyas that improved data quality increase value. The second is that maintaining high quality information is a best practice that mature organizations do.

Today I’ll focus on the first type of justification, especially in the context of scoping the program. In recent customer interactions, we have found that there are a number of questions that need answers to validate the business justification. Actually, in most of our discussions, it turns out that the questions are not even asked, let alone answered. So I thought it might be worth throwing some of these questions out there:

One can focus on specific areas of the business or target the organization as a whole. In either case, one question to ask is whether the actual data quality tasks to be performed are “value-driven”? In my book I detail a number of dimensions of value that can be used to link data quality problems to business impacts, including financial, trust, productivity, and trust. I am starting to work on a project that will provide another level of detail about assessing business impacts, and will keep you updated as the project nears completion.

The second question is determining the degree to which the expected value depends on the data and how much depends on alternate factors. Another way of asking this is what additional organizational changes are necessary to derive the benefit of high quality data?

The third question is intended to assess your success – are there business performance measures linked to the quality of the data?

And the last one for today is more of a conundrum: Identifying a data quality issue with business impacts and eliminating the root cause of the data quality issue can be seen in two different lights. In the first, by eliminating the data issue you increase value by reducing or eliminating any negative business impact. In the second, by eliminating the data issue you are also eliminating the possibility for negative business impact. And in that second light, if there is no possibility for a negative impact, what is the value of the continued “operational data quality” investment?

The first view is critical at the beginning of the project, when funding is needed, but the second view becomes more of an issue later in the program lifecycle when seeking continuing funding. At that later stage, it appears as if there is a large investment for reduced value, forcing the data quality team to consider best methods to communicate the ongoing value. Mor eon this soon…

Find and Fix? A Question About Data Quality Metrics

Data profiling can be an excellent approach to identifying latent issues and errors hidden in your data. We have seen a number of clients using data profiling as the first step in defining data quality metrics and using those metrics for reporting via scorecards and dashboards.

And if I can identify a problem and I can define a rule for determining that the problem exists, should I not be able to fix the problem? Here is a question, though: once I fix the root cause of the problem, do I need to still keep checking if the problem has occured?

More on this in an upcoming post; contact me if you have thoughts…